path: "tensorflow.losses"
tf_module {
  member {
    name: "Reduction"
    mtype: "<type \'type\'>"
  }
  member_method {
    name: "absolute_difference"
    argspec: "args=[\'labels\', \'predictions\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], "
  }
  member_method {
    name: "add_loss"
    argspec: "args=[\'loss\', \'loss_collection\'], varargs=None, keywords=None, defaults=[\'losses\'], "
  }
  member_method {
    name: "compute_weighted_loss"
    argspec: "args=[\'losses\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], "
  }
  member_method {
    name: "cosine_distance"
    argspec: "args=[\'labels\', \'predictions\', \'axis\', \'weights\', \'scope\', \'loss_collection\', \'reduction\', \'dim\'], varargs=None, keywords=None, defaults=[\'None\', \'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\', \'None\'], "
  }
  member_method {
    name: "get_losses"
    argspec: "args=[\'scope\', \'loss_collection\'], varargs=None, keywords=None, defaults=[\'None\', \'losses\'], "
  }
  member_method {
    name: "get_regularization_loss"
    argspec: "args=[\'scope\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'total_regularization_loss\'], "
  }
  member_method {
    name: "get_regularization_losses"
    argspec: "args=[\'scope\'], varargs=None, keywords=None, defaults=[\'None\'], "
  }
  member_method {
    name: "get_total_loss"
    argspec: "args=[\'add_regularization_losses\', \'name\', \'scope\'], varargs=None, keywords=None, defaults=[\'True\', \'total_loss\', \'None\'], "
  }
  member_method {
    name: "hinge_loss"
    argspec: "args=[\'labels\', \'logits\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], "
  }
  member_method {
    name: "huber_loss"
    argspec: "args=[\'labels\', \'predictions\', \'weights\', \'delta\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], "
  }
  member_method {
    name: "log_loss"
    argspec: "args=[\'labels\', \'predictions\', \'weights\', \'epsilon\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'1e-07\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], "
  }
  member_method {
    name: "mean_pairwise_squared_error"
    argspec: "args=[\'labels\', \'predictions\', \'weights\', \'scope\', \'loss_collection\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\'], "
  }
  member_method {
    name: "mean_squared_error"
    argspec: "args=[\'labels\', \'predictions\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], "
  }
  member_method {
    name: "sigmoid_cross_entropy"
    argspec: "args=[\'multi_class_labels\', \'logits\', \'weights\', \'label_smoothing\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], "
  }
  member_method {
    name: "softmax_cross_entropy"
    argspec: "args=[\'onehot_labels\', \'logits\', \'weights\', \'label_smoothing\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], "
  }
  member_method {
    name: "sparse_softmax_cross_entropy"
    argspec: "args=[\'labels\', \'logits\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], "
  }
}
